Vaccination is currently the most effective strategy for the mitigation of the COVID-19 pandemic. mRNA vaccines trigger the immune system to produce neutralizing antibodies (NAbs) against SARS-CoV-2 spike proteins. However, the underlying molecular processes affecting immune response after vaccination remain poorly understood, while there is significant heterogeneity in the immune response among individuals. Metabolomics have often been used to provide a deeper understanding of immune cell responses, but in the context of COVID-19 vaccination such data are scarce. Mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR)-based metabolomics were used to provide insights based on the baseline metabolic profile and metabolic alterations induced after mRNA vaccination in paired blood plasma samples collected and analysed before the first and second vaccination and at 3 months post first dose. Based on the level of NAbs just before the second dose, two groups, “low” and “high” responders, were defined. Distinct plasma metabolic profiles were observed in relation to the level of immune response, highlighting the role of amino acid metabolism and the lipid profile as predictive markers of response to vaccination. Furthermore, levels of plasma ceramides along with certain amino acids could emerge as predictive biomarkers of response and severity of inflammation.

Melanoma is the most aggressive type of skin cancer, leading to metabolic rewiring and enhancement of metastatic transformation. Efforts to improve its early and accurate diagnosis are largely based on preclinical models and especially cell lines. Hence, we herein present a combinational Nuclear Magnetic Resonance (NMR)- and Ultra High Performance Liquid Chromatography-High-Resolution Tandem Mass Spectrometry (UHPLC-HRMS/MS)-mediated untargeted metabolomic profiling of melanoma cells, to landscape metabolic alterations likely controlling metastasis. The cell lines WM115 and WM2664, which belong to the same patient, were examined, with WM115 being derived from a primary, pre-metastatic, tumor and WM2664 clonally expanded from lymph-node metastases. Metabolite samples were analyzed using NMR and UHPLC-HRMS. Multivariate statistical analysis of high resolution NMR and MS (positive and negative ionization) results was performed by Principal Component Analysis (PCA), Partial Least Squares-Discriminant Analysis (PLS-DA) and Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA), while metastasis-related biomarkers were determined on the basis of VIP lists, S-plots and Student’s t-tests. Receiver Operating Characteristic (ROC) curves of NMR and MS data revealed significantly differentiated metabolite profiles for each cell line, with WM115 being mainly characterized by upregulated levels of phosphocholine, choline, guanosine and inosine. Interestingly, WM2664 showed notably increased contents of hypoxanthine, myo-inositol, glutamic acid, organic acids, purines, pyrimidines, AMP, ADP, ATP and UDP(s), thus indicating the critical roles of purine, pyrimidine and amino acid metabolism during human melanoma metastasis.

Background: Urothelial bladder cancer (UBC) is one of the cancers with the highest mortality rate and prevalence worldwide; however, the clinical management of the disease remains challenging. Metabolomics has emerged as a powerful tool with beneficial applications in cancer biology and thus can provide new insights on the underlying mechanisms of UBC progression and/or reveal novel diagnostic and therapeutic schemes. Methods: A collection of four human UBC cell lines that critically reflect the different malignancy grades of UBC was employed; RT4 (grade I), RT112 (grade II), T24 (grade III), and TCCSUP (grade IV). They were examined using Nuclear Magnetic Resonance, Mass Spectrometry, and advanced statistical approaches, with the goal of creating new metabolic profiles that are mechanistically associated with UBC progression toward metastasis. Results: Distinct metabolic profiles were observed for each cell line group, with T24 (grade III) cells exhibiting the most abundant metabolite contents. AMP and creatine phosphate were highly increased in the T24 cell line compared to the RT4 (grade I) cell line, indicating the major energetic transformation to which UBC cells are being subjected during metastasis. Thymosin β4 and β10 were also profiled with grade-specific patterns of expression, strongly suggesting the importance of actin-cytoskeleton dynamics for UBC advancement to metastatic and drug-tolerant forms. Conclusions: The present study unveils a novel and putatively druggable metabolic signature that holds strong promise for early diagnosis and the successful chemotherapy of UBC disease.

Quantitation of chromophore-free analytes is always a challenge. To this purpose, derivatization of the analyte constitutes a common strategy, leading to a product with a strong signal. In the current study, a novel xanthone analogue was utilized for the first time for the derivatization of pregabalin, a model analyte with a primary amine moiety that lacks a chromophore. The fact that only the xanthene-based derivative, formed after the derivatization reaction fluoresces, enables avoiding its chromatographic separation from the reagent and thus reducing the analysis time of a series of samples in 1–2 min via a plate reader. The reaction conditions were optimized via a central composite design (CCD), with fluorescence signal as the measure of the yield. The following factors that affect the derivatization reaction were chosen: (a) temperature, (b) reaction time, and (c) triethylamine solution volume used to drive the reaction to completion. After the identification of the optimal conditions, the method was validated according to ICH guidelines, using a fluorescence plate reader for signal measurement (λ_ex = 540, λ_em = 615 nm). Finally, the newly developed high-throughput method was applied to the determination of drug content in pregabalin bulk.

Numerous genetic variants have been identified by genome-wide association studies as being associated with colorectal cancer (CRC) risk. Metabolome-wide association analysis was performed for 187 CRC-associated genetic variants using genomic data and untargeted ¹H nuclear magnetic resonance urine metabolomics from 1951 Airwave Health Monitoring Study participants. We identified statistically significant associations between seven CRC single-nucleotide polymorphisms (SNPs) and urinary metabolites. This included SNPs within or close to RHPN2 with sucrose (P = 1.2 × 10^-7), SLC6A18 with amino acids (P = 6.9 × 10^-5 with tyrosine, P = 9.9 × 10^-5 with leucine), and MAP2K5 and BMP2 with gut microbial metabolites (P = 1.6 × 10^-4 and P = 4.4 × 10^-4). The most significant correlation was followed by functional experiments in Caco-2 colon cancer cells. CRISPR-mediated knockout of a 48-nt RHPN2 intronic region containing rs10411210 in colon cancer cells compromised cell growth. RNA sequencing was performed in the two sets of clones (3 edited and 3 unedited) followed by pathway enrichment, and gene ontology analysis depicted extensive deregulation of genes (448 up- and 195 downregulated) involved in cell division and several metabolic processes. Overall, these findings demonstrate that integrating genetic and metabolomic data highlights the importance of the RHPN2 intronic locus in CRC potentially through metabolic processes affecting excretion of dietary and other metabolites.